Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "52" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 38 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 36 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459853 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 8.945802 | 7.805099 | 2.633265 | 2.411362 | 6.707154 | -0.997001 | 1.575837 | -0.074574 | 0.7546 | 0.7074 | 0.4157 | 2.957104 | 2.623897 |
| 2459852 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 7.635977 | 8.127596 | 0.723584 | 1.729026 | 3.263063 | 0.045207 | -0.143855 | 1.314798 | 0.8411 | 0.8450 | 0.2370 | 4.087998 | 4.065087 |
| 2459851 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 8.272481 | 7.230625 | 2.527672 | 2.730557 | 3.074247 | 0.304081 | 0.285697 | 1.675377 | 0.7787 | 0.7666 | 0.3264 | 3.748794 | 3.388753 |
| 2459850 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 8.923400 | 8.348063 | 2.886105 | 2.421972 | 6.168597 | -0.997932 | 1.235952 | 0.027002 | 0.7574 | 0.7726 | 0.3492 | 2.753331 | 2.492724 |
| 2459849 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 10.145615 | 9.183918 | 3.752743 | 1.097697 | 7.980807 | -1.145375 | 2.455919 | -0.143462 | 0.7561 | 0.7658 | 0.3532 | 3.902878 | 3.470523 |
| 2459848 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 9.937407 | 8.996734 | 2.385784 | 2.299420 | 5.206761 | -1.050432 | 0.592566 | -0.197225 | 0.7333 | 0.7654 | 0.3799 | 3.319557 | 3.035785 |
| 2459847 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 10.614490 | 9.843744 | -0.140176 | 0.049349 | 2.227190 | -0.308617 | 0.335741 | -0.625581 | 0.7454 | 0.7073 | 0.4199 | 3.001608 | 2.739376 |
| 2459846 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 9.579192 | 7.999066 | -0.213549 | -0.302428 | 2.946932 | 0.676763 | 1.478402 | -0.431798 | 0.8507 | 0.7016 | 0.4670 | 3.311625 | 2.789124 |
| 2459845 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 11.924296 | 10.856385 | 1.345797 | 0.159094 | 16.580859 | -0.183193 | 25.786917 | 3.759216 | 0.7432 | 0.7652 | 0.3714 | 3.794448 | 4.233953 |
| 2459844 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 22.225167 | 15.986336 | 20.234231 | 16.670714 | 177.126098 | 105.426069 | 132.362854 | 79.531240 | 0.0255 | 0.0230 | 0.0011 | nan | nan |
| 2459843 | RF_maintenance | 100.00% | 0.66% | 0.66% | 0.00% | 100.00% | 0.00% | 9.546064 | 10.165408 | 1.891305 | -0.757352 | 23.625247 | 1.929301 | 83.450993 | 4.862747 | 0.7542 | 0.7661 | 0.3771 | 4.611769 | 4.215529 |
| 2459840 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 76.036534 | 59.781583 | 16.425332 | 14.210240 | 8.396959 | 4.757040 | 20.873789 | 15.423021 | 0.0222 | 0.0202 | 0.0014 | nan | nan |
| 2459839 | RF_maintenance | 100.00% | - | - | - | - | - | 19.807632 | 15.603500 | 39.548296 | 34.758432 | 3.076247 | 1.956930 | 27.884262 | 20.352981 | nan | nan | nan | nan | nan |
| 2459838 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 10.452127 | 9.060696 | 1.433126 | 0.146363 | 11.854586 | 0.658077 | 1.351619 | 0.506674 | 0.7679 | 0.7214 | 0.3819 | 5.935476 | 5.408078 |
| 2459836 | RF_maintenance | - | 100.00% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.0322 | 0.0310 | 0.0011 | nan | nan |
| 2459835 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | -0.997738 | -0.517669 | 2.253785 | 0.575359 | 5.068024 | -0.547627 | 3.679457 | 1.469349 | 0.0313 | 0.0310 | 0.0008 | nan | nan |
| 2459833 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 6.236887 | 4.518939 | 8.272565 | 6.938327 | 3.613295 | 0.007756 | 14.062350 | 8.401923 | 0.0246 | 0.0236 | 0.0010 | nan | nan |
| 2459832 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 13.189805 | 10.735482 | 0.194753 | -0.298396 | 3.616832 | -0.860846 | 1.382069 | 0.149262 | 0.8172 | 0.5532 | 0.5652 | 3.407247 | 3.042549 |
| 2459831 | RF_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 19.736520 | 15.695324 | 42.291937 | 37.273925 | 4.404646 | 2.384198 | 20.629681 | 14.446747 | 0.0224 | 0.0205 | 0.0011 | nan | nan |
| 2459830 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 12.086160 | 10.311010 | 1.231779 | -0.082815 | 3.661880 | -1.326788 | 1.882282 | 0.655664 | 0.8168 | 0.5667 | 0.5426 | 5.306006 | 4.572636 |
| 2459829 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 16.061188 | 14.063229 | 3.177883 | 0.211435 | 8.988212 | -0.553395 | 5.505592 | 2.048000 | 0.7707 | 0.6843 | 0.3938 | 16.508811 | 14.445772 |
| 2459828 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 10.142289 | 8.176693 | 2.526502 | 0.281375 | 1.964637 | -0.868571 | 4.112226 | 3.496295 | 0.8132 | 0.5738 | 0.5287 | 5.340046 | 5.260242 |
| 2459827 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 12.401689 | 10.976249 | 2.199552 | 0.249250 | 5.241054 | 0.371830 | 0.294880 | -0.639949 | 0.7768 | 0.6914 | 0.3945 | 14.603412 | 12.336965 |
| 2459826 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 8.826469 | 7.650326 | 1.103716 | 0.006731 | 2.723052 | -1.407010 | 2.424307 | 1.818370 | 0.8114 | 0.5906 | 0.5076 | 8.996297 | 10.436701 |
| 2459825 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 9.315766 | 8.064467 | 0.300468 | 0.114912 | 0.834837 | -0.942607 | -0.369216 | -0.612798 | 0.8123 | 0.5994 | 0.5081 | 6.248912 | 5.394365 |
| 2459824 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 11.371693 | 9.818135 | 1.153257 | 0.284989 | 0.568116 | -0.934492 | 0.747127 | 0.704082 | 0.7511 | 0.7535 | 0.3429 | 8.601732 | 7.327025 |
| 2459823 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 6.450111 | 5.767260 | 0.122859 | 0.068141 | 2.015915 | -0.895345 | 0.771404 | 4.048611 | 0.7826 | 0.6635 | 0.4409 | 52.452986 | 35.555434 |
| 2459822 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 8.319739 | 7.560948 | 1.050507 | 0.138901 | 1.581811 | -1.037183 | 0.712223 | -0.647768 | 0.8139 | 0.6285 | 0.4928 | 5.456208 | 5.543786 |
| 2459821 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 8.458586 | 7.667712 | 1.106520 | -0.038851 | 1.663133 | -0.788239 | -0.696761 | -0.733707 | 0.8102 | 0.6468 | 0.4946 | 5.786361 | 4.396454 |
| 2459820 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 11.281427 | 10.550016 | 2.023719 | 0.143290 | 15.623165 | 0.415849 | 3.406238 | 2.252369 | 0.7901 | 0.7176 | 0.3963 | 3.883301 | 3.999510 |
| 2459817 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 6.872654 | 6.261614 | 0.648206 | -0.110738 | 2.843233 | -0.673592 | -0.074841 | -0.048197 | 0.8228 | 0.6882 | 0.4850 | 3.967907 | 3.887831 |
| 2459816 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 8.712973 | 7.448970 | 0.281801 | -0.080330 | 1.054136 | -0.961280 | 1.139650 | 2.265762 | 0.8504 | 0.6133 | 0.5818 | 3.530607 | 3.194201 |
| 2459815 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 6.374297 | 5.974706 | -0.200005 | -0.097487 | 1.162073 | 0.327081 | 1.793406 | 2.600394 | 0.8166 | 0.6977 | 0.4932 | 3.621074 | 3.210298 |
| 2459814 | RF_maintenance | 0.00% | - | - | - | - | - | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459813 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | 100.00% | 0.00% | 12.835179 | 13.858857 | 0.968687 | -0.300875 | 16.885977 | 0.874819 | 5.417107 | 3.775288 | 0.8116 | 0.7662 | 0.3687 | 7.787622 | 10.424492 |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Shape | 8.945802 | 7.805099 | 8.945802 | 2.411362 | 2.633265 | -0.997001 | 6.707154 | -0.074574 | 1.575837 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | nn Shape | 8.127596 | 7.635977 | 8.127596 | 0.723584 | 1.729026 | 3.263063 | 0.045207 | -0.143855 | 1.314798 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Shape | 8.272481 | 8.272481 | 7.230625 | 2.527672 | 2.730557 | 3.074247 | 0.304081 | 0.285697 | 1.675377 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Shape | 8.923400 | 8.923400 | 8.348063 | 2.886105 | 2.421972 | 6.168597 | -0.997932 | 1.235952 | 0.027002 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Shape | 10.145615 | 10.145615 | 9.183918 | 3.752743 | 1.097697 | 7.980807 | -1.145375 | 2.455919 | -0.143462 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Shape | 9.937407 | 8.996734 | 9.937407 | 2.299420 | 2.385784 | -1.050432 | 5.206761 | -0.197225 | 0.592566 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Shape | 10.614490 | 9.843744 | 10.614490 | 0.049349 | -0.140176 | -0.308617 | 2.227190 | -0.625581 | 0.335741 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Shape | 9.579192 | 9.579192 | 7.999066 | -0.213549 | -0.302428 | 2.946932 | 0.676763 | 1.478402 | -0.431798 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Temporal Discontinuties | 25.786917 | 10.856385 | 11.924296 | 0.159094 | 1.345797 | -0.183193 | 16.580859 | 3.759216 | 25.786917 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Temporal Variability | 177.126098 | 22.225167 | 15.986336 | 20.234231 | 16.670714 | 177.126098 | 105.426069 | 132.362854 | 79.531240 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Temporal Discontinuties | 83.450993 | 10.165408 | 9.546064 | -0.757352 | 1.891305 | 1.929301 | 23.625247 | 4.862747 | 83.450993 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Shape | 76.036534 | 76.036534 | 59.781583 | 16.425332 | 14.210240 | 8.396959 | 4.757040 | 20.873789 | 15.423021 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Power | 39.548296 | 15.603500 | 19.807632 | 34.758432 | 39.548296 | 1.956930 | 3.076247 | 20.352981 | 27.884262 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Temporal Variability | 11.854586 | 9.060696 | 10.452127 | 0.146363 | 1.433126 | 0.658077 | 11.854586 | 0.506674 | 1.351619 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Temporal Variability | 5.068024 | -0.517669 | -0.997738 | 0.575359 | 2.253785 | -0.547627 | 5.068024 | 1.469349 | 3.679457 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Temporal Discontinuties | 14.062350 | 4.518939 | 6.236887 | 6.938327 | 8.272565 | 0.007756 | 3.613295 | 8.401923 | 14.062350 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Shape | 13.189805 | 13.189805 | 10.735482 | 0.194753 | -0.298396 | 3.616832 | -0.860846 | 1.382069 | 0.149262 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Power | 42.291937 | 19.736520 | 15.695324 | 42.291937 | 37.273925 | 4.404646 | 2.384198 | 20.629681 | 14.446747 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Shape | 12.086160 | 12.086160 | 10.311010 | 1.231779 | -0.082815 | 3.661880 | -1.326788 | 1.882282 | 0.655664 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Shape | 16.061188 | 14.063229 | 16.061188 | 0.211435 | 3.177883 | -0.553395 | 8.988212 | 2.048000 | 5.505592 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Shape | 10.142289 | 8.176693 | 10.142289 | 0.281375 | 2.526502 | -0.868571 | 1.964637 | 3.496295 | 4.112226 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Shape | 12.401689 | 12.401689 | 10.976249 | 2.199552 | 0.249250 | 5.241054 | 0.371830 | 0.294880 | -0.639949 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Shape | 8.826469 | 7.650326 | 8.826469 | 0.006731 | 1.103716 | -1.407010 | 2.723052 | 1.818370 | 2.424307 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Shape | 9.315766 | 8.064467 | 9.315766 | 0.114912 | 0.300468 | -0.942607 | 0.834837 | -0.612798 | -0.369216 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Shape | 11.371693 | 11.371693 | 9.818135 | 1.153257 | 0.284989 | 0.568116 | -0.934492 | 0.747127 | 0.704082 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Shape | 6.450111 | 5.767260 | 6.450111 | 0.068141 | 0.122859 | -0.895345 | 2.015915 | 4.048611 | 0.771404 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Shape | 8.319739 | 8.319739 | 7.560948 | 1.050507 | 0.138901 | 1.581811 | -1.037183 | 0.712223 | -0.647768 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Shape | 8.458586 | 7.667712 | 8.458586 | -0.038851 | 1.106520 | -0.788239 | 1.663133 | -0.733707 | -0.696761 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Temporal Variability | 15.623165 | 11.281427 | 10.550016 | 2.023719 | 0.143290 | 15.623165 | 0.415849 | 3.406238 | 2.252369 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Shape | 6.872654 | 6.872654 | 6.261614 | 0.648206 | -0.110738 | 2.843233 | -0.673592 | -0.074841 | -0.048197 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Shape | 8.712973 | 7.448970 | 8.712973 | -0.080330 | 0.281801 | -0.961280 | 1.054136 | 2.265762 | 1.139650 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Shape | 6.374297 | 5.974706 | 6.374297 | -0.097487 | -0.200005 | 0.327081 | 1.162073 | 2.600394 | 1.793406 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 52 | N03 | RF_maintenance | ee Temporal Variability | 16.885977 | 13.858857 | 12.835179 | -0.300875 | 0.968687 | 0.874819 | 16.885977 | 3.775288 | 5.417107 |